TWI898387B - Body composition measurement method and measurement apparatus therefor - Google Patents

Abstract

A body composition measurement method is provided. The body composition measurement method uses bioelectrical impedance analysis (BIA) together with a measurement model to measure the composition of a body part comprising a plurality of muscle groups. The body composition measurement method comprises: measuring muscle impedance values corresponding to each of the muscle groups; setting up an impedance relation function for these muscle groups based on relative positions of these muscle groups in the body; using the muscle impedance values and the impedance relation function to generate a simulated impedance value; and feeding the simulated impedance value in the measurement model to generate composition data of the body part. A body composition measurement apparatus is also provided.

Description

Body composition measurement method and body composition measurement device

This case relates to a body composition measurement method and a body composition measurement device.

Sarcopenia is a major cause of disability in older adults. It is characterized by a persistent and widespread loss of skeletal muscle mass and function, which can lead to disability, decreased quality of life, and even an increased risk of death.

Muscle mass is a key diagnostic criterion for sarcopenia. While there are professional measuring instruments on the market that can accurately measure muscle mass, these instruments are expensive and cumbersome to operate, making them impractical for everyday use. Furthermore, while some wearable devices do provide muscle mass measurements, the data they provide is inaccurate and cannot be used as a reliable reference standard.

This application provides a method for measuring body composition. This method utilizes bioimpedance analysis in conjunction with a measurement model to measure the composition of a body part to be measured. The measurement model corresponds to the part to be measured, and the part to be measured includes multiple muscle groups. This method includes: measuring these muscle groups to generate muscle impedance values corresponding to each muscle group; establishing an impedance relationship for these muscle groups based on their relative positions on the body; generating a simulated impedance value using these muscle impedance values and the impedance relationship; and inputting the simulated impedance value into the measurement model to generate composition data corresponding to the part to be measured.

This case also provides a body composition measurement device. This body composition measurement device uses bioimpedance analysis to measure the composition of a part to be measured on the body. This part to be measured includes multiple muscle groups. This body composition measurement device includes a measuring device and a processing device. The measuring device has a measuring surface for sequentially pressing against these muscle groups to generate muscle impedance values corresponding to each muscle group; the processing device stores a measurement model corresponding to the part to be measured and an impedance relationship corresponding to the part to be measured, and has a processing unit. This processing unit is used to: receive these muscle impedance values; generate a simulated impedance value using these muscle impedance values and the impedance relationship; and input the simulated impedance value into the measurement model to generate composition data corresponding to the part to be measured.

The body composition measurement device and method provided in this application allows users to measure the muscle impedance of individual muscle groups using a simple measurement device. This device then converts the impedance values corresponding to the measured area into simulated impedance values, which serve as input to a measurement model to estimate the compositional data of the measured area. This body composition measurement device is easy to operate and convenient for daily use. Furthermore, the method of converting the muscle impedance values of individual muscle groups into simulated impedance values for the measured area effectively addresses the inability of conventional wearable devices to measure specific body parts and their lack of measurement accuracy.

The following detailed description of the specific embodiments of this invention is accompanied by schematic diagrams. The advantages and features of this invention will become more apparent from the following description and the scope of the patent application. It should be noted that the figures are highly simplified and not to exact scale, and are intended solely to facilitate and clearly illustrate the embodiments of this invention.

The first figure is a flow chart of a body composition measurement method provided according to an embodiment of the present invention.

This body composition measurement method utilizes bioelectrical impedance analysis (BIA) in conjunction with a measurement model to measure the composition of a target body part of a subject. This method is particularly suitable for measuring parts that include multiple muscle groups. For example, the target part can be an arm, leg, trunk, or even the entire body. The measurement model used in this method corresponds to the target body part being measured.

In one embodiment, the measurement model can be built using supervised machine learning. In another embodiment, the measurement model can include a nonlinear regression equation to predict the composition of the measured area of the subject. However, the present invention is not limited to this.

Specifically, a set of measurement data and measurement components of a subject's target area can be used as training data to establish this measurement model. The measurement data can be the impedance value of the target area measured by a high-precision body composition meter, and the measurement components can be the test results generated by the high-precision body composition meter on the target area.

This body composition measurement method includes the following steps.

First, as described in step S120, the subject's personal information D1 is received. This personal information D1 includes age, height, weight, gender, and dominant hand.

Subsequently, as described in step S130, multiple muscle groups at the target location of the subject are measured to generate muscle impedance values corresponding to each muscle group. This step utilizes bioimpedance measurement technology. Specifically, a small amount of electrical current is passed through the body, and electrodes are used to measure the muscle impedance values corresponding to the target muscle groups.

Next, as described in step S140, an impedance relationship is established for these muscle groups based on their relative positions on the body. In one embodiment, this step establishes an impedance relationship based on the relative positions of multiple muscle groups on the body using resistors connected in series or parallel. Then, as described in step S150, a simulated impedance value is generated using these muscle impedance values and the impedance relationship.

For example, the second figure shows the relationship between the muscle groups and the impedance relationship of the left arm. As shown in the figure, the impedance corresponding to the left arm can be simulated using the following impedance relationship:

Y1=Z1+(Z2xZ3)/(Z2+Z3)+Z4…Formula 1

Among them, Y1 is the simulated impedance value of the left arm muscle group, Z1 is the impedance value of the left shoulder muscle P1, Z2 is the impedance value of the left biceps muscle P2, Z3 is the impedance value of the left triceps muscle P3, and Z4 is the impedance value of the left forearm muscle P4.

In addition, the third figure shows the corresponding relationship between the muscle groups of the left leg and the impedance relationship. As shown in the figure, the impedance corresponding to the left leg can be simulated using the following impedance relationship:

Y2=Z5+(Z6xZ7)/(Z6+Z7)+Z8…Formula 2

Among them, Y2 is the simulated impedance value of the left leg muscle group, Z5 is the impedance value of the left gluteus muscle P5, Z6 is the impedance value of the left quadriceps femoris P6, Z7 is the impedance value of the left biceps femoris P7, and Z8 is the impedance value of the left calf muscle P8.

In addition, Figure 4 shows the corresponding relationship between the muscle groups of the trunk and the impedance relationship. As shown in the figure, the impedance corresponding to the trunk can be simulated using the following impedance relationship:

Y3=((Z9+Z10)x(Z11+Z12))/((Z9+Z10)+(Z11+Z12))…Formula 3

Among them, Y3 is the simulated impedance value of the trunk muscle group, Z9 is the impedance value of the chest muscle P9, Z10 is the impedance value of the abdominal muscle P10, Z11 is the impedance value of the upper back muscle P11, and Z12 is the impedance value of the lower back muscle P12.

Figure 5 shows the relationship between muscle groups and impedance. As shown in the figure, the impedance of the whole body can be simulated using the following impedance relationship:

Y4=Z13+Z14+Z15…Formula 4

Among them, Y4 is the simulated impedance value of the muscle groups of the whole body, Z13 is the impedance value of the left triceps P13, Z14 is the impedance value of the abdominal muscle P14, and Z15 is the impedance value of the left quadriceps P15.

Subsequently, as described in step S160, the simulated impedance value is input into the measurement model corresponding to the measured area to generate compositional data corresponding to the measured area. In one embodiment, step S160 may also include individual information D1 into the measurement model to clearly distinguish individual differences and improve accuracy. This compositional data may, for example, include muscle mass, water content, fat mass, and bone mass of the measured area.

For the simulated impedance value Y1 corresponding to the left arm calculated in the second figure, this step involves inputting the simulated impedance value Y1 into the measurement model corresponding to the left arm to generate compositional data for the subject's left arm. Similarly, for the simulated impedance value Y2 corresponding to the left leg calculated in the third figure, this step involves inputting the simulated impedance value Y2 into the measurement model corresponding to the left leg to generate compositional data for the subject's left leg. For the simulated impedance value Y3 corresponding to the trunk calculated in the fourth figure, this step involves inputting the simulated impedance value Y3 into the measurement model corresponding to the trunk to generate compositional data for the subject's trunk. Regarding the simulated impedance value Y4 corresponding to the whole body calculated in FIG. 5 , this step is to input the simulated impedance value Y4 into the measurement model corresponding to the whole body to generate the whole body composition data of the subject.

Figures 6 and 7 are three-dimensional schematic diagrams of a body composition measurement device 600 according to an embodiment of the present invention. Figure 8 is a functional block diagram of the body composition measurement device 600 shown in Figure 6.

This body composition measurement device 600 utilizes bioimpedance analysis in conjunction with at least one pre-established measurement model M1, M2, M3, and M4 (four measurement models are shown, corresponding to the arms, legs, trunk, and entire body, respectively, but the present invention is not limited thereto) to measure the composition of a target body region of a subject. For example, the target body region can be an arm, leg, trunk, or entire body, all of which contain multiple muscle groups.

The body composition measurement device 600 includes a measuring device 620 and a processing device 640. The measuring device 620 is a handheld device that allows the subject to perform self-measurement. The processing device 640 is a wearable device, such as a wristband or watch.

The measuring device 620 has a receiving groove 622 and a measuring surface A1. The receiving groove 622 is suitable for accommodating the processing device 640. The measuring surface A1 is suitable for being placed against the target muscle group for measurement to generate the corresponding muscle impedance value.

The processing device 640 includes an operation interface 642, a storage unit 644, and a processing unit 646. The operation interface 642 is used to receive a user input, a selection command S1, to confirm the area to be measured (i.e., the area to be measured) and the subject's personal information D1. Personal information D1 includes age, height, weight, gender, and dominant handedness.

Storage unit 644 stores multiple pre-created measurement models M1, M2, M3, and M4 to meet measurement needs. These measurement models M1, M2, M3, and M4 correspond to different areas to be measured. For example, processing device 640 may store a measurement model M1 corresponding to the left arm, a measurement model M2 corresponding to the left leg, a measurement model M3 corresponding to the trunk, and a measurement model M4 corresponding to the entire body. Furthermore, in one embodiment, processing device 640 may also store multiple measurement models (not shown) corresponding to individual muscle groups in the body to meet user needs.

The back and sides of the processing device 640 are equipped with electrodes suitable for measuring the impedance of muscle groups. Only the electrodes 647 located on the sides of the processing device 640 are shown in the figure.

When the processing device 640 is installed in the receiving chamber 622, the processing device 640 is electrically coupled to the measuring surface A1 of the measuring device 620 to obtain measurement data D2 from the measuring device 620. Specifically, electrodes (not shown) may be provided on the back of the processing device 640, and corresponding contacts (not shown) may be provided within the receiving chamber 622, with the contacts electrically coupled to the electrodes on the measuring surface A1. When the processing device 640 is installed in the receiving chamber 622, the electrodes on the back of the processing device 640 are electrically coupled to the electrodes on the measuring surface A1 via the contacts, thereby obtaining measurement data D2 from the measuring device 620.

The aforementioned embodiment utilizes a physical connection between the processing device 640 and the measuring device 620 to obtain measurement data D2. However, the present invention is not limited to this. In other embodiments, the processing device 640 may also communicate with the measuring device 620 wirelessly to obtain measurement data D2. Furthermore, the processing device 640 is not limited to a wearable device. For example, the processing device 640 may also be a smartphone.

During measurement, the user enters a selection command S1 through the user interface 642 to confirm the desired measurement area and the subject's personal information D1. Based on the selection command S1, the processing unit 646 selects the desired measurement model and the impedance relationship to be applied. Based on the selected measurement area, the processing unit 646 provides a measurement prompt indicating the muscle group to be measured. This prompt can be displayed on the screen 648 or provided as a voice prompt.

Subsequently, the user may install the processing device 640 in the receiving slot 622 and then sequentially place the measuring device 620 against multiple muscle groups at the site to be measured to measure the muscle impedance value of each muscle group.

After completing the measurement of each muscle group corresponding to the measured area, processing unit 646 receives these muscle impedance values and uses them with the impedance relationship to calculate a simulated impedance value corresponding to the measured area. This simulated impedance value and user-entered personal information D1 are then input into the measurement model to generate component data corresponding to the measured area. This component data may include, for example, muscle mass, water content, fat mass, and bone mass of the measured area.

The processing unit 646 can be, for example, a central processing unit (CPU), a graphics processing unit (GPU), an application-specific integrated circuit (ASIC), or other processing units dedicated to deep learning.

Please also refer to FIG. 9 , which illustrates the processing device 640 of FIG. 6 mounted on the measuring device 620 for measurement. As shown in the figure, after the user mounts the processing device 640 on the measuring device 620 , they can then hold the measuring device 620 against the measuring surface A1 of the muscle group to be measured for measurement.

For example, referring to Figures 2 to 5, if the measured area is the left arm, the measurement device 620 can sequentially measure the impedance values of the left shoulder muscle P1, the left biceps brachii muscle P2, the left triceps brachii muscle P3, and the left forearm muscle P4. If the measured area is the left leg, the measurement device 620 can sequentially measure the impedance values of the left gluteus muscle P5, the left quadriceps femoris muscle P6, the left biceps femoris muscle P7, and the left calf muscle P8. If the measured area is the trunk, the measurement device 620 can sequentially measure the impedance values of the pectoral muscle P9, the abdominal muscle P10, the upper back muscle P11, and the lower back muscle P12. Furthermore, if the measured area is the entire body, the measurement device 620 can sequentially measure the impedance values of the left triceps brachii muscle P13, the abdominal muscle P14, and the left quadriceps femoris muscle P15.

Please refer to FIG. 10 , which shows a schematic diagram of the processing device 640 in FIG. 6 being used solely for body composition measurement.

In one embodiment, electrodes (not shown) are provided on the surface of processing device 640. These electrodes can be located on the back and sides of the processing device 640 housing. When the user wears processing device 640 on their non-dominant hand, they can use the fingers of their dominant hand to touch the electrodes 647 on the side of the processing device 640 housing (see FIG7 ). This creates a current loop between the dominant and non-dominant hands. The electrodes measure the impedance of the connection from the dominant hand through the trunk to the non-dominant hand. This impedance value can be used to calculate the subject's overall body composition data.

Through the body composition measurement equipment 600 and body composition measurement method provided in this case, a simple measuring device 620 can be used to measure the muscle impedance values Z1, Z2, Z3, Z4, Z5, Z6, Z7, Z8, Z9, Z10, Z11, Z12, Z13, Z14, Z15 of each muscle group, and then convert the simulated impedance values Y1, Y2, Y3, Y4 corresponding to the measured part into inputs of the measurement models M1, M2, M3, M4 to estimate the composition data of the measured part. This body composition measurement device 600 is easy to operate and convenient for daily use. Furthermore, by converting the muscle impedance values Z1, Z2, Z3, Z4, Z5, Z6, Z7, Z8, Z9, Z10, Z11, Z12, Z13, Z14, and Z15 of each muscle group into simulated impedance values Y1, Y2, Y3, and Y4 for the measured body part, this device effectively addresses the inability of conventional wearable devices to measure specific body parts and the resulting inaccurate measurements.

The above is merely a preferred embodiment of this case and does not limit this case in any way. Any equivalent replacement, modification, or other alteration of the technical means and technical content disclosed in this case by any technical personnel within the scope of the technical means of this case shall be deemed to be within the content of the technical means of this case and shall remain within the scope of protection of this case.

600: Body composition measurement device 620: Measuring device 622: Storage tank 640: Processing device 642: User interface 644: Storage unit 646: Processing unit 647: Electrode 648: Screen S120, S130, S140, S150, S160: Steps A1: Measuring surface P1: Left shoulder muscle P2: Left biceps brachii P3, P13: Left triceps brachii P4: Left forearm muscle P5: Left gluteus muscle P6, P15: Left quadriceps femoris P7: Left biceps femoris P8: Left calf muscle P9: Chest muscle P10, P14: Abdominal muscles P11: Upper back muscles P12: Lower back muscles Z1, Z2, Z3, Z4, Z5, Z6, Z7, Z8, Z9, Z10, Z11, Z12, Z13, Z14, Z15: Muscle impedance values M1, M2, M3, M4: Measurement model Y1, Y2, Y3, Y4: Simulated impedance values S1: Selection command D1: Personal information D2: Measurement data

Figure 1 is a flow chart of a body composition measurement method according to an embodiment of the present invention; Figure 2 shows the correspondence between the muscle groups of the left arm and the impedance equation; Figure 3 shows the correspondence between the muscle groups of the left leg and the impedance equation; Figure 4 shows the correspondence between the muscle groups of the trunk and the impedance equation; Figure 5 shows the correspondence between the muscle groups of the entire body and the impedance equation; Figures 6 and 7 are three-dimensional schematic diagrams of a body composition measurement device according to an embodiment of the present invention; Figure 8 is a functional block diagram of the body composition measurement device shown in Figure 6; Figure 9 shows the processing device of Figure 6 mounted on a measurement device for measurement; and Figure 10 shows the processing device of Figure 6 used alone for body composition measurement.

S120, S130, S140, S150, S160: Steps

Claims (10)

A body composition measurement method utilizes bioimpedance analysis in conjunction with a measurement model to measure the composition of a measured portion of a subject's body. The measurement model corresponds to the measured portion, and the measured portion includes multiple muscle groups. The body composition measurement method comprises: measuring the muscle groups to generate muscle impedance values corresponding to each muscle group; establishing an impedance relationship between the muscle groups based on the relative positions of the muscle groups on the body; generating a simulated impedance value using the muscle impedance values and the impedance relationship; and inputting the simulated impedance value into the measurement model to generate composition data corresponding to the measured portion. The measured portion is an arm, a leg, a trunk, or the entire body. When the measured portion is an arm, the impedance relationship is: Y1=Z1+(Z2xZ3)/(Z2+Z3)+Z4…, where Y1 is the simulated impedance value of the arm muscle group, Z1 is the impedance value of the shoulder muscle, Z2 is the impedance value of the arm biceps brachii muscle, Z3 is the impedance value of the arm triceps brachii muscle, and Z4 is the impedance value of the forearm muscle. When the measured part is the leg, the impedance relationship is: Y2=Z5+(Z6xZ7)/(Z6+Z7)+Z8…, where Y2 is the simulated impedance value of the leg muscle group, Z5 is the impedance value of the gluteal muscle, Z6 is the impedance value of the quadriceps femoris muscle, Z7 is the impedance value of the biceps femoris muscle, and Z8 is the impedance value of the calf muscle. When the measured part is the trunk, the impedance relationship is: Y3=((Z9+Z10)x(Z11+Z12))/((Z9+Z10)+(Z11+Z12)), where Y3 is the simulated impedance value of the trunk muscle group, Z9 is the impedance value of the pectoral muscles, Z10 is the impedance value of the abdominal muscles, Z11 is the impedance value of the upper back muscles, and Z12 is the impedance value of the lower back muscles. When the measured part is the entire body, the impedance relationship is: Y4=Z13+Z14+Z15, where Y4 is the simulated impedance value of the entire body muscle group, Z13 is the impedance value of the triceps brachii, Z14 is the impedance value of the abdominal muscles, and Z15 is the impedance value of the quadriceps femoris. The body composition measurement method as described in claim 1, wherein the measurement model is established using supervised machine learning. The body composition measurement method as described in claim 2, wherein the measurement model includes a nonlinear regression equation. The body composition measurement method as described in claim 1, wherein the step of inputting the simulated impedance value into the measurement model to generate composition data corresponding to the part to be measured is inputting the simulated impedance value and the personal information of the subject into the measurement model to generate the composition data corresponding to the part to be measured. The body composition measurement method as described in claim 4, wherein the personal information includes age, height, weight, gender, and dominant hand. A body composition measurement device utilizes bioimpedance analysis to measure the composition of a body part to be measured, wherein the part to be measured includes multiple muscle groups. The body composition measurement device includes: a measurement device having a measurement surface for sequentially contacting the muscle groups to generate muscle impedance values corresponding to each muscle group; a processing device having a measurement model corresponding to the part to be measured and an impedance relationship corresponding to the part to be measured, and a processing unit for: receiving the muscle impedance values; generating a simulated impedance value using the muscle impedance values and the impedance relationship; and inputting the simulated impedance value into the measurement model to generate composition data corresponding to the part to be measured. The part to be measured is an arm, a leg, a trunk, or the entire body. When the part to be measured is an arm, the impedance relationship is: Y1=Z1+(Z2xZ3)/(Z2+Z3)+Z4…, where Y1 is the simulated impedance value of the arm muscle group, Z1 is the impedance value of the shoulder muscle, Z2 is the impedance value of the arm biceps brachii muscle, Z3 is the impedance value of the arm triceps brachii muscle, and Z4 is the impedance value of the forearm muscle. When the measured part is the leg, the impedance relationship is: Y2=Z5+(Z6xZ7)/(Z6+Z7)+Z8…, where Y2 is the simulated impedance value of the leg muscle group, Z5 is the impedance value of the gluteal muscle, Z6 is the impedance value of the quadriceps femoris muscle, Z7 is the impedance value of the biceps femoris muscle, and Z8 is the impedance value of the calf muscle. When the measured part is the trunk, the impedance relationship is: Y3=((Z9+Z10)x(Z11+Z12))/((Z9+Z10)+(Z11+Z12)), where Y3 is the simulated impedance value of the trunk muscle group, Z9 is the impedance value of the pectoral muscles, Z10 is the impedance value of the abdominal muscles, Z11 is the impedance value of the upper back muscles, and Z12 is the impedance value of the lower back muscles. When the measured part is the entire body, the impedance relationship is: Y4=Z13+Z14+Z15, where Y4 is the simulated impedance value of the entire body muscle group, Z13 is the impedance value of the triceps brachii, Z14 is the impedance value of the abdominal muscles, and Z15 is the impedance value of the quadriceps femoris. A body composition measuring device as described in claim 6, wherein the measuring device is a handheld device and the processing device is a wearable device. A body component measuring apparatus as described in claim 7, wherein the measuring device has a receiving groove, which is suitable for accommodating the processing device and electrically coupling the processing device to the measuring surface. The body composition measuring device as described in claim 6, wherein the processing device is a smart phone. The body composition measurement device as described in claim 6, wherein the processing device further includes an operating interface for receiving a selection instruction and personal information of a subject, the processing unit selects the part to be measured based on the selection instruction, and the processing unit inputs the simulated impedance value and the personal information into the measurement model to generate composition data corresponding to the part to be measured.